Emergent Properties of the HNF4α-PPARγ Network May Drive Consequent Phenotypic Plasticity in NAFLD

Abstract

Non-alcoholic fatty liver disease (NAFLD) is the most common form of chronic liver disease in adults and children. It is characterized by excessive accumulation of lipids in the hepatocytes of patients without any excess alcohol intake. With a global presence of 24% and limited therapeutic options, the disease burden of NAFLD is increasing. Thus, it becomes imperative to attempt to understand the dynamics of disease progression at a systems-level. Here, we decoded the emergent dynamics of underlying gene regulatory networks that were identified to drive the initiation and the progression of NAFLD. We developed a mathematical model to elucidate the dynamics of the HNF4α-PPARγ gene regulatory network. Our simulations reveal that this network can enable multiple co-existing phenotypes under certain biological conditions: an adipocyte, a hepatocyte, and a “hybrid” adipocyte-like state of the hepatocyte. These phenotypes may also switch among each other, thus enabling phenotypic plasticity and consequently leading to simultaneous deregulation of the levels of molecules that maintain a hepatic identity and/or facilitate a partial or complete acquisition of adipocytic traits. These predicted trends are supported by the analysis of clinical data, further substantiating the putative role of phenotypic plasticity in driving NAFLD. Our results unravel how the emergent dynamics of underlying regulatory networks can promote phenotypic plasticity, thereby propelling the clinically observed changes in gene expression often associated with NAFLD.

Topological signatures in regulatory network enable phenotypic heterogeneity in small cell lung cancer

“…Analysis based on the influence matrix established the concept of two 'teams' of players that are effectively inhibiting each other and activating themselves, thereby forming an 'effective' self-activating toggle switch (SATS). Such SATS have been shown to be multistable and tend to underlie phenotypic plasticity and heterogeneity in multiple cell-fate decisions (Guantes and Poyatos, 2008;Lu et al, 2013;Sahoo et al, 2020;Zhou and Huang, 2011). This 'teaming up' can potentially explain a) why single-edge perturbations in the WT SCLC network are rarely disruptive in terms of steady state distributions (because as long as an 'effective' mutual inhibition between the two teams and 'effective' self-activation in the teams is maintained, the phenotypes are likely to be robust attractors), and b) why despite such dense and complicated (33 nodes, 357 edges) network, we obtain only four steady states (because the 'latent' network topology is fundamentally simplistic).…”

Prioritizing and characterizing functionally relevant genes across human tissues

“…Upon closer inspection of the top TRGs, we found a large number of master regulators highly characteristic of the tissue were scored highly. For instance, PPARG and HNF1A, master regulators for adipogenesis and the hepatic cell fate respectively [33], are among the top TRGs for the adipose and the liver tissues respectively and are found to be implicated in NAFLD [34] and steatosis associated liver cancer [33]. Similarly, insulin is the highest scored gene (FUGUE score: 0.98) in the pancreas tissue.…”

Prioritizing and characterizing functionally relevant genes across human tissues

“…Upon closer inspection of the top TRGs, we found a large number of master regulators highly characteristic of the tissue were scored highly. For instance, PPARG and HNF1A, master regulators for adipogenesis and the hepatic cell fate respectively (32), are among the top TRGs for the adipose and the liver tissues respectively and are found to be implicated in NAFLD (33) and steatosis associated liver cancer (32). Similarly, insulin is the highest scored gene (FUGUE score: 0.98) in the pancreas tissue.…”